Companies often use a data reconciliation process to compare and/or verify data from different data sources. The data in these sources may be stored in different technical structures but may be linked. For example, data may be sorted into subset data but may be derived from a common master data set. The reconciliation process may be used to verify that the subset data is accurate which may also provide a verification of the accuracy of the master data set. Examples of a subset data sources include accounting ledgers, inventory listings, or the like that may be derived from a common set of data.
One goal of the reconciliation process may be to identify data in one subset that does not reconcile with data in another subset, even though both subsets are derived from the same master data set. Violations may occur due to customization errors, data entry errors, program errors, etc. This reconciliation process can also be part of a periodic audit.
Conventional reconciliation programs do not provide a flexible interface to customize the technical structure of the data source and/or the connection between the data. As a consequence, these programs are limited for use with only two specific data sources. Moreover, known reconciliation processes are further limited to pre-determined rules or checks of data dependencies that do not provide any flexibility for different customer requirements.
A more robust and flexible reconciliation process is needed.